Implementation Timeline from PoC to IoT Deployment

Working with an IoT enabler platform brings forth a number of challenges. Enabler platforms provide the fully-fledged infrastructure and the environment for companies to build their own IoT products and set up IoT cases. While it makes it possible to get started at a much greater pace and get to mass IoT deployment with significantly less upfront investment, the enabler platform does not absolve organizations from the need to strategize, plan carefully, and map out their IoT effort from the edge all the way up to the cloud.

For one thing, the platform itself will only generate value if organizations start off on the right foot. This involves setting up a viable data strategy, scoping up their maturity level, and realigning according to current need.

Identifying a winning data strategy

The first line of defence is the data strategy and finding ways to make use of all data coming from multiple different endpoints, consolidating the data coming from older-generation machines, legacy equipment, and any new smart device. So you need a platform that can handle and unify data coming from a connected device that use different data formats and a variety of communication protocols. Data sharing has to be tackled from the outset so that you are freely able to share data with cloud systems and IT.

Settling on a healthy ratio between edge and cloud

Another part of the data strategy is settling on a healthy ratio between edge and cloud analytics. The amount of data that has to be pre-processed at the edge and the data aimed for high-quality IoT analytics in the cloud need to be distributed in a way that makes sense. The right balance between edge and cloud will be different for each company and for each business case.

Edge computing comes with zero latency and enables quicker reaction times. Data pre-processing capabilities at the IoT edge make it possible to collect data fast, perform basic analytics, and take action on that data immediately, right where it matters the most. Cloud computing, on the other hand, enables in-depth analysis. The collected IoT edge data can be combined with data coming from other manufacturing sites or other, non-IoT data sources. The analyses that ensue from this allow for greater insight into the data and the creation of a larger data picture.

So the fast-paced IoT analytics at the edge are best combined with long-term, in-depth insight into the data thanks to analyses performed in the cloud.

Scaling the solution

But then again, even after all data-related and connectivity issues are resolved, your organization will still have to tackle the scaling challenge. A small-scale PoC with a limited number of devices may be working just about fine. Once you begin to add thousands of devices from multiple manufacturing sites, however, things begin to look differently. This is the reason why so often you hear of an IoT project that does not make it past the PoC stage. The difficulties in large-scale IoT deployment often appear insurmountable.

But a winning IoT implementation will give you the ability to connect any number of devices at multiple locations. You will deploy OTA apps to your assets globally from within a unified platform that unites all data to serve as your single source of truth.

Your ideal industrial IoT platform will overcome these hurdles by enabling organization-wide data accessibility. This will give you full transparency across the entire value chain, and full connectivity of all industrial assets and systems. In terms of operations, this means uninterrupted data collection and real-time data processing. The consequence is a seamless data journey all the way to advanced analytics in the cloud, machine learning models building, and instant OTA model deployment, among others.

PoC / Pilot Deployment

IronFlock allows you to get started on your PoC fast. Results will show within less than two months. You begin with just one edge device to collect, transform, and analyze the data. Then you visualize in dashboards, and generate initial insights just by looking at the standard KPIs. This is how you test the ground to make sure that your large scale IoT deployments will work all the way from the edge to the cloud.

Deployment to Production

With the completion of the pilot stage, you begin to build a mature use case. This involves machines in production, rolling out a production-ready IoT application, and settling on the right KPIs. During the production stage, you collect data across the entire manufacturing site. This way, you derive decisions based on IoT analytics, and integrate with a variety of local and cloud systems. This is how you test whether your IoT deployment is working in real conditions. And you can see how it enhances the existing operations.

Scale-out Implementation

During the scale-out stage, you move your solution across various manufacturing sites. You handle multiple heterogeneous devices from within a single IoT device management suite for orchestrating apps and devices. And you monitor how the solution is performing at a greater scale and optimize where needed.

The Benefits

• a simple instant setup and no programming effort needed,
• connecting any Docker-capable IoT device or piece of equipment with just a few clicks, zero programming,
• online and offline rollouts over-the-air take place instantly at the push of a button,
• set up a data source and begin streaming data into the platform in just a few minutes,
• shape a viable PoC that solves a real problem,
• continue to optimize to better understand the data and identify trends,
• connect to additional data sources to enrich the existing use cases,
• set viable metrics for predictive maintenance, anomaly detection, asset tracking, overall equipment, effectiveness, operational efficiency, and many other standard scenarios,
• set up alerts for uptime, downtime, and other custom behaviour parameters, identify bottlenecks and outliers,
• build machine learning models, package these as IoT apps, and roll them out to the IoT edge over the air. All in one continuous gesture.
• seamlessly integrate with the typical cloud systems, including AWS, Microsoft Azure, Google Cloud, and others,
• achieve full data transparency and accessibility. You will share data across the organization, collaborate with IoT engineers and data scientists for quicker outcomes.

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